if __name__ == '__main__':
label_list = ["B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "O"]
label_map = {idx: label for idx, label in enumerate(label_list)}
model = hub.Module(
name='ernie_tiny',
version='2.0.1',
task='token-cls',
label_map=label_map,
)
train_dataset = hub.datasets.MSRA_NER(tokenizer=model.get_tokenizer(),
max_seq_len=128,
mode='train')
dev_dataset = hub.datasets.MSRA_NER(tokenizer=model.get_tokenizer(),
max_seq_len=50,
mode='dev')
optimizer = paddle.optimizer.AdamW(learning_rate=5e-5,
parameters=model.parameters())
trainer = hub.Trainer(model,
optimizer,
checkpoint_dir='token_cls_save_dir',
use_gpu=True)
trainer.train(train_dataset,
epochs=3,
batch_size=32,
eval_dataset=dev_dataset,
save_interval=1)
if __name__ == '__main__':
model = hub.Module(name='ernie_tiny',
version='2.0.1',
task='text-matching')
tokenizer = model.get_tokenizer()
train_dataset = LCQMC(tokenizer=tokenizer,
max_seq_len=args.max_seq_len,
mode='train')
dev_dataset = LCQMC(tokenizer=tokenizer,
max_seq_len=args.max_seq_len,
mode='dev')
test_dataset = LCQMC(tokenizer=tokenizer,
max_seq_len=args.max_seq_len,
mode='test')
optimizer = paddle.optimizer.AdamW(learning_rate=args.learning_rate,
parameters=model.parameters())
trainer = hub.Trainer(model,
optimizer,
checkpoint_dir=args.checkpoint_dir,
use_gpu=args.use_gpu)
trainer.train(
train_dataset,
epochs=args.num_epoch,
batch_size=args.batch_size,
eval_dataset=dev_dataset,
save_interval=args.save_interval,
)
trainer.evaluate(test_dataset, batch_size=args.batch_size)
import paddlehub as hub
model = hub.Module(name='ernie_tiny',
version='2.0.1',
task='seq-cls',
num_classes=2)
train_dataset = hub.datasets.ChnSentiCorp(tokenizer=model.get_tokenizer(),
max_seq_len=128,
mode='train')
dev_dataset = hub.datasets.ChnSentiCorp(tokenizer=model.get_tokenizer(),
max_seq_len=128,
mode='dev')
import paddle
optimizer = paddle.optimizer.Adam(learning_rate=5e-5,
parameters=model.parameters())
trainer = hub.Trainer(model,
optimizer,
checkpoint_dir='test_ernie_text_cls',
use_gpu=False)
trainer.train(train_dataset,
epochs=3,
batch_size=32,
eval_dataset=dev_dataset,
save_interval=1)
